1. Acknowledgements The work presented in this poster was carried out within the LIGO Scientific Collaboration (LSC). The methods and results presented here are under review and are potentially subject to change. The opinions expressed here are those of the author and not necessarily those of the LSC. The author gratefully acknowledges the support of the United States National Science Foundation for the construction and operation of the LIGO Laboratory, which provided support for this work. The author also thanks the California Institute of Technology Summer Undergraduate Fellowship program for funding this work. This poster has LIGO document number LIGO-G1400005-v5. Acknowledgements The work presented in this poster was carried out within the LIGO Scientific Collaboration (LSC). The methods and results presented here are under review and are potentially subject to change. The opinions expressed here are those of the author and not necessarily those of the LSC. The author gratefully acknowledges the support of the United States National Science Foundation for the construction and operation of the LIGO Laboratory, which provided support for this work. The author also thanks the California Institute of Technology Summer Undergraduate Fellowship program for funding this work. This poster has LIGO document number LIGO-G1400005-v5. Contact Ashley Disbrow adisbrow@andrew.cmu.edu Contact Ashley Disbrow adisbrow@andrew.cmu.edu Aim Search for compact binary coalescence injections in LIGO S5 data Compare results with expected values given in the original documentation Update the documentation with precise and accurate descriptions of the injections found in the data Aim Search for compact binary coalescence injections in LIGO S5 data Compare results with expected values given in the original documentation Update the documentation with precise and accurate descriptions of the injections found in the data Introduction The Laser Interferometer Gravitational Wave Observatory (LIGO) is using ground-breaking technology to directly detect gravitational wave signals from astronomical sources. Thus far, gravitational waves have never been directly detected, but as detector sensitivities increase, we expect to be able to detect gravitational wave signals from distant galaxies. One particular signal we look for in the detector is generated by compact binary coalescence. Compact binary coalescence occurs when two compact massive objects, such as black holes or neutron stars, orbit one another and spiral in toward one another until they collide. The gravitational wave signal, one type of many which theory predicts LIGO may detect, has a recognizable waveform. The LIGO Scientific Collaboration is preparing to release the data collected during Science Run 5 (S5) to the public. S5 contains data taken at the three LIGO observatories from 2005-2007. Periodically throughout the S5 data collection, hardware injections were added to the data at planned times, for testing and calibration, by actuating the mirrors in the arms of the interferometers. In preparation for the release, the original documentation detailing interferometer conditions and the success or failure of hardware injections needs to be updated. Introduction The Laser Interferometer Gravitational Wave Observatory (LIGO) is using ground-breaking technology to directly detect gravitational wave signals from astronomical sources. Thus far, gravitational waves have never been directly detected, but as detector sensitivities increase, we expect to be able to detect gravitational wave signals from distant galaxies. One particular signal we look for in the detector is generated by compact binary coalescence. Compact binary coalescence occurs when two compact massive objects, such as black holes or neutron stars, orbit one another and spiral in toward one another until they collide. The gravitational wave signal, one type of many which theory predicts LIGO may detect, has a recognizable waveform. The LIGO Scientific Collaboration is preparing to release the data collected during Science Run 5 (S5) to the public. S5 contains data taken at the three LIGO observatories from 2005-2007. Periodically throughout the S5 data collection, hardware injections were added to the data at planned times, for testing and calibration, by actuating the mirrors in the arms of the interferometers. In preparation for the release, the original documentation detailing interferometer conditions and the success or failure of hardware injections needs to be updated. Method Create compact binary coalescence waveform templates Our templates model the strain/Hz in the detector as a compact binary coalescence signal is detected. Strain is a measurement of how space-time is distorted by passing gravitational waves. It is related to the amplitude, A, frequency, f, and phase, Ψ, of the source according to equation 1. (1) Find the injections using a matched filter search Figure 2 shows a spectrogram of the injected signal from a neutron star – neutron star binary coalescence. We take a segment of data where we expect to see an injection, like that in figure 2, and Fourier transform it into the frequency domain. We then cross correlate the template with the data to find the time of highest correlation. This is our hardware injection. From this correlation and the parameters of the model, we now know the time of the injection and the mass parameters of the coalescing objects. Determine the recovered signal to noise ratio (SNR) of the injection The amplitude signal to noise ratio of the search output, ρ m (t), is related to the time domain search output, z m (t), and σ m, a measure of the instrument sensitivity according to equation 2. (2) Determine the predicted signal to noise ratio of the injection To determine the predicted signal to noise ratio, we look at the S5 injection documentation and find what effective distance, D eff, was used to model the injection signal. Equation 3 gives predicted signal to noise ratio, with the same σ m sensitivity measurement. (3) Method Create compact binary coalescence waveform templates Our templates model the strain/Hz in the detector as a compact binary coalescence signal is detected. Strain is a measurement of how space-time is distorted by passing gravitational waves. It is related to the amplitude, A, frequency, f, and phase, Ψ, of the source according to equation 1. (1) Find the injections using a matched filter search Figure 2 shows a spectrogram of the injected signal from a neutron star – neutron star binary coalescence. We take a segment of data where we expect to see an injection, like that in figure 2, and Fourier transform it into the frequency domain. We then cross correlate the template with the data to find the time of highest correlation. This is our hardware injection. From this correlation and the parameters of the model, we now know the time of the injection and the mass parameters of the coalescing objects. Determine the recovered signal to noise ratio (SNR) of the injection The amplitude signal to noise ratio of the search output, ρ m (t), is related to the time domain search output, z m (t), and σ m, a measure of the instrument sensitivity according to equation 2. (2) Determine the predicted signal to noise ratio of the injection To determine the predicted signal to noise ratio, we look at the S5 injection documentation and find what effective distance, D eff, was used to model the injection signal. Equation 3 gives predicted signal to noise ratio, with the same σ m sensitivity measurement. (3) Results Figure 3 above plots all hardware injections marked as successful in the original documentation. The recovered signal to noise ratio is plotted versus the predicted signal to noise ratio. A few injections are not recovered due to a failure during the hardware injection process. This is an important result and we have now updated the documentation to reflect these findings. Figure 4 plots all hardware injections recorded as unsuccessful. All of these are low recovered signal to noise ratio, as expected because they failed to inject into the data correctly, except for one. We have successfully recovered an injection that we did not expect to see according its unsuccessful label. Upon closer inspection of the time surrounding this injection, we find that the injection was successful, though the interferometer went out of “science mode” shortly after the injection finished. (“Science mode” data is considered good, and all other data is not used.) Figure 5 shows a spectrogram of this mislabelled injection. It is a 10 solar mass – 10 solar mass binary coalescence that occurs at a 10 Mpc distance from the detector. Results Figure 3 above plots all hardware injections marked as successful in the original documentation. The recovered signal to noise ratio is plotted versus the predicted signal to noise ratio. A few injections are not recovered due to a failure during the hardware injection process. This is an important result and we have now updated the documentation to reflect these findings. Figure 4 plots all hardware injections recorded as unsuccessful. All of these are low recovered signal to noise ratio, as expected because they failed to inject into the data correctly, except for one. We have successfully recovered an injection that we did not expect to see according its unsuccessful label. Upon closer inspection of the time surrounding this injection, we find that the injection was successful, though the interferometer went out of “science mode” shortly after the injection finished. (“Science mode” data is considered good, and all other data is not used.) Figure 5 shows a spectrogram of this mislabelled injection. It is a 10 solar mass – 10 solar mass binary coalescence that occurs at a 10 Mpc distance from the detector. Conclusion Table 1 summarizes our findings for all injections in each of the three LIGO detectors. In each detector, we recover most of the successful injections, with the exception of a few. We also recover a couple injection that we do not expect to find. We have updated the hardware injection documentation to reflect these findings in preparation for the public data release. Conclusion Table 1 summarizes our findings for all injections in each of the three LIGO detectors. In each detector, we recover most of the successful injections, with the exception of a few. We also recover a couple injection that we do not expect to find. We have updated the hardware injection documentation to reflect these findings in preparation for the public data release. Recovering hardware injections in LIGO S5 data Ashley Disbrow 1, Jonah Kanner 2, Roy Williams 2, Michele Vallisneri 2,3, Alan J. Weinstein 2 1. Carnegie Mellon University, Pittsburgh, PA. 2. California Institute of Technology, Pasadena, CA. 3. Jet Propulsion Laboratory, Pasadena, CA. Figure 5: Spectrogram of a 10 – 10 solar mass compact binary coalescence hardware injection at a 10 Mpc distance. The detector went out of science mode shortly after the injection completed. Figure 3: This plot contains all injections marked as successful in the original S5 documentation from the 2km detector in Hanford, WA. The SNR our search recovers is plotted vs. the SNR predicted by the old documentation’s distance parameters. The blue line represents equal recovered and predicted SNR. Figure 2: Spectrogram of a simulated 1.4 – 1.4 solar mass neutron star binary undergoing compact binary coalescence at a distance of 0.1 Mpc. Notice the increase in orbital frequency as the stars spiral inward toward one another. This is called a “chirp” because the frequency is audible when played with speakers. Figure 1: The LIGO detector in Hanford, WA. Each arm stretches on for 4 km. 1.4 – 1.4 solar mass binary 3 – 3 solar mass binary 10 – 10 solar mass binary 1.4 – 10 solar mass binary 1.4 – 1.4 solar mass binary 3 – 3 solar mass binary 10 – 10 solar mass binary 1.4 – 10 solar mass binary Figure 4: This plot contains all injections marked as unsuccessful in the original S5 documentation from the 2km detector in Hanford, WA. The SNR our search recovers is plotted vs. the SNR predicted by the old documentation’s distance parameters. The blue line represents equal recovered and predicted SNR. DetectorH1H2H2L1 Total # Injections 120012821271 Successful Injections, Data Available 870929770 Expected to be Recovered 614333545 Recovered 608322538 Successful Injections, Data Unavailable 211914 Unsuccessful Injections, Data Available 464551 Recovered 132 Unsuccessful Injections, Data Unavailable 263289436 Table 1: The total number of scheduled injections at each detector is broken down into categories here. We expect to recover injections with an SNR > 8. We consider injections with an SNR > 6 to be successfully recovered. To get more information about gravitational wave astrophysics or a PDF version of this poster, check out my AAS Extras page by scanning the QR code to the left or visiting: http://extras.aas.org/meeting/aas223/248/248.12